Introduction
In the recent times, every business must know how to prepare business data for AI agents and Salesforce. AI agents are considered one of the best practical tools for businesses. It can automate many tasks that may improve the efficiency of the company’s workflow. It solves customer query, helps with sales, and automate the processes done repeatedly.
But the most important element business owners tend to forget is the fact that AI agents work on the basis of strong data provided by the organisation.
Salesforce requires authentic data for different use cases. The quality and credibility check of the data is important because the outcome depends on it.
So, before building an AI agent, it is worth getting your data in order.
1. Start With the Use Case
Do not revise the complete database of your organization, instead understand your requirement. Evaluate what all tasks does the company require AI agent to carry out.
For instance, the customer representative might require:
- Customer details
- Previous cases
- Order information
- Product details
- Warranty information
- FAQs and support documents
A sales agent may need account records, previous purchases, opportunities, pricing and customer interactions.
Once you know the job, you can identify exactly which data the agent needs.
2. Find Where Your Data Lives
Data location is never fixed. It is divided into different paths and locations.
You may have customer data in Salesforce and documents in Google Drive and provide support to conversations in another platform.
Here you should make a list of all of your data sources. Identify the location of each. Understand if the information is structured or not. Keep a check on CRM records, PDFs, emails, and documents.
Salesforce supports structured and unstructured data.
The mentioned strategy exposes the loopholes in database become it forms into AI related issue.
3. Clean Your Existing Data
You might think this part of AI project is not worth trying. But it is the most important one.
Look for:
- Duplicate customer records
- Missing information
- Incorrect contact details
- Outdated records
- Different formats for the same information
- Old product or pricing details
Authentic and structured data gives your AI agent a much better head start. Salesforce notes that inconsistent names, formats and other data-quality problems can affect the accuracy and reliability of agent responses.
4. Decide What Your Source of Truth Is
Imagine your return policy is available in three different documents, and each one says something slightly different.
Which one should the AI use?
Before connecting data to an agent, decide which system or document is the source of truth for important information.
This reduces confusion and makes it easier to maintain your AI system later.
5. Don’t Ignore Your Documents
Some of your most useful business information may not be sitting in a database.
It could be in:
- PDFs
- Product manuals
- FAQs
- Emails
- Support conversations
- Training documents
- Company policies
- Website content
These sources can be extremely useful for AI agents because they contain the kind of detailed information customers and employees often ask about.
Agentforce Data Libraries can use sources such as knowledge articles, uploaded files and web content to ground AI responses in your business information.
Just make sure those documents are current before connecting them.
6. Give AI the Right Context
It is not enough to just have data. The agent needs clear understanding of how to relate different pieces of information to each other.
For instance:
- Customer
- Orders
- Products Cases
- Previous conversations
When these relationships are available, the agent can understand more than just a customer’s name or order number.
Salesforce Data 360 can bring information from different sources together and help create a more unified view of business data.
The goal isn’t to give AI more information. It is to give it relevant information with the right context.
7. Use Grounding for More Reliable Answers
You don’t want an AI agent guessing about your business.
Grounding gives the agent access to trusted company information when it needs to answer a question. Salesforce describes grounding as adding relevant business information to the context used by an AI model.
8. Protect Sensitive Information
Not every piece of business data should be available to an AI agent.
Review what the agent can access and make sure it only sees information necessary for its job.
Think carefully about:
- Customer information
- Financial data
- Employee records
- Contracts
- Confidential documents
- Internal business information
Agentforce includes security and governance features, but businesses should still define their own access rules and AI policies.
9. Test Before Going Live
Don’t assume your agent will work perfectly because the data has been connected.
Test it with different questions.
Ask:
- “What is the product warranty?”
- “What is your return policy?”
- “Show me this customer’s recent cases.”
Then check whether the answers are accurate, relevant and based on the right information.
It is also important to test what happens when the agent doesn’t know the answer. A clear handoff to a human is often better than an incorrect answer.
10. Keep Your Data Updated
Preparing your data isn’t a one-time task.
Your products, prices, policies and customer information will continue to change. Old documents will be replaced by new information.
Keep a regular check on process for reviewing:
- Data quality
- Knowledge articles
- Documents
- Permissions
- Agent responses
- New data sources
That way, your AI agent doesn’t become outdated six months after launch.
Conclusion
The success of an AI agent depends on more than the AI model itself. The quality of the information behind it matters just as much.
If you’re planning to use Salesforce Agentforce, start with your data. Begin with one useful business use case, prepare the data around it and improve from there.
